Insightforge | AI & Data Science on Instagram: "In three dimensions, linear regression can be visualized as a plane that best fits the data. When we move into higher dimensions, this idea extends to an n-dimensional hyperplane that represents the model’s predictions. Training this model relies on calculus and linear algebra, along with a way to measure how well the hyperplane matches the data. This measurement is the loss function, most commonly the mean squared error (MSE) or similar variants.

Linear regression is a statistical technique used to describe the relationship between a dependent variable and one or more independent variables. It works by finding the straight line that best fits the data, represented by an equation with a slope (or multiple slopes) and an intercept. To fit this line, the algorithm estimates the model parameters in a way that minimizes the gap between the actual data points and the model’s predictions. These gaps are called residuals, which represent the dif